This project focuses on coordination of eye and hand movements in carrying out simple tasks. Using gaze-contingent displays, we simulate the kinds of retinal damage that are associated with glaucoma, retinitis pigmentosa and age-related macular degeneration, evaluate how damage affects eye-hand coordination and measure how quickly subjects learn to compensate. In three series of experiments, we track eye movements and hand movements and their interaction. Bayesian decision theory provides a very natural way to model and better understand how humans plan movements. The first goal of this research is to extend existing Bayesian decision-theoretic models of movement planning to include eye and hand movements and their interactions. The result will be a predictive model of human planning of movement. A second goal is to better understand how the visuo-motor system learns to compensate for damage due to retinal disease or injury and how to speed such compensation. PUBLIC HEALTH RELEVANCE: We are studying how humans plan movement in realistic tasks that require coordination of hand and eye. Exploring the limits of movement planning in normal, healthy humans gives us insight into how well or poorly they will cope with disease, aging or injury.